In recent years, as an emerging means for biological recognition, voiceprint recognition has received great attention due to the security and convenience it provides. Voiceprint recognition is used to recognize and authenticate a person's identity utilizing unique information contained in each person's voice based on physiological differences in sizes and forms in the vocal organs of different persons, such as lungs, trachea, vocal cords, oral cavities, nasal cavities and pharyngeal cavities. However, in reality, voice acoustic characteristics of each person are neither absolute nor constant, but are jointly affected by external factors such as environments and used devices, and by intrinsic factors such as individual physical condition, emotional fluctuation and speech style change. In addition, researches have shown that human voice acoustic characteristics also may change with age. Thus, tracing dynamic changes of voiceprint characteristics of a user so that a voiceprint model of the user can be adaptively updated according to newly added voice samples, is of great significance to enhance the functionalities of a voiceprint recognition system.
At present, when a user is authenticated, the voiceprint model generated based on the voice data input at the time of user registration is generally used. However, this model cannot be adaptively updated, and thus this method for authenticating the user may lead to incorrect authentication result as time goes on.